In wireless sensor networks a large amount of data is collected for each node. The challenge of trans-ferring these data to a sink, because of energy constraints, requires suitable techniques such as datacompression. Transform-based compression, e.g. Discrete Wavelet Transform (DWT), are very popularin this field. These methods behave well enough if there is a correlation in data. However, especiallyfor environmental measurements, data may not be correlated. In this work, we propose two approachesbased on F-transform, a recent fuzzy approximation technique. We evaluate our approaches with Dis-crete Wavelet Transform on publicly available real-world data sets. The comparative study shows thecapabilities of our approaches, which allow a higher data compression rate with a lower distortion, evenif data are not correlated.
Multisignal 1D-compression by F-transform for wireless sensor networks applications
GAETA, Matteo;LOIA, Vincenzo;TOMASIELLO, Stefania
2015
Abstract
In wireless sensor networks a large amount of data is collected for each node. The challenge of trans-ferring these data to a sink, because of energy constraints, requires suitable techniques such as datacompression. Transform-based compression, e.g. Discrete Wavelet Transform (DWT), are very popularin this field. These methods behave well enough if there is a correlation in data. However, especiallyfor environmental measurements, data may not be correlated. In this work, we propose two approachesbased on F-transform, a recent fuzzy approximation technique. We evaluate our approaches with Dis-crete Wavelet Transform on publicly available real-world data sets. The comparative study shows thecapabilities of our approaches, which allow a higher data compression rate with a lower distortion, evenif data are not correlated.File | Dimensione | Formato | |
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Multisignal 1–D compression - Applied Soft Computing.pdf
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Descrizione: Articolo Multisignal 1–D compression by F–transform for wireless sensor networks applications
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